{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Working with Time Series"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas was originally developed in the context of financial modeling, so as you might expect, it contains an extensive set of tools for working with dates, times, and time-indexed data.\n",
    "Date and time data comes in a few flavors, which we will discuss here:\n",
    "\n",
    "- *Timestamps* reference particular moments in time (e.g., July 4th, 2021 at 7:00 a.m.).\n",
    "- *Time intervals* and *periods* reference a length of time between a particular beginning and end point; for example, the month of June 2021. Periods usually reference a special case of time intervals in which each interval is of uniform length and does not overlap (e.g., 24-hour-long periods comprising days).\n",
    "- *Time deltas* or *durations* reference an exact length of time (e.g., a duration of 22.56 seconds).\n",
    "\n",
    "This chapter will introduce how to work with each of these types of date/time data in Pandas.\n",
    "This is by no means a complete guide to the time series tools available in Python or Pandas, but instead is intended as a broad overview of how you as a user should approach working with time series.\n",
    "We will start with a brief discussion of tools for dealing with dates and times in Python, before moving more specifically to a discussion of the tools provided by Pandas.\n",
    "After listing some resources that go into more depth, we will review some short examples of working with time series data in Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dates and Times in Python\n",
    "\n",
    "The Python world has a number of available representations of dates, times, deltas, and time spans.\n",
    "While the time series tools provided by Pandas tend to be the most useful for data science applications, it is helpful to see their relationship to other tools used in Python."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Native Python Dates and Times: datetime and dateutil\n",
    "\n",
    "Python's basic objects for working with dates and times reside in the built-in `datetime` module.\n",
    "Along with the third-party `dateutil` module, you can use this to quickly perform a host of useful functionalities on dates and times.\n",
    "For example, you can manually build a date using the `datetime` type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "datetime(year=2021, month=7, day=4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or, using the `dateutil` module, you can parse dates from a variety of string formats:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 7, 4, 0, 0)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from dateutil import parser\n",
    "date = parser.parse(\"4th of July, 2021\")\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once you have a `datetime` object, you can do things like printing the day of the week:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sunday'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we've used one of the standard string format codes for printing dates (`'%A'`), which you can read about in the [`strftime` section](https://docs.python.org/3/library/datetime.html#strftime-and-strptime-behavior) of Python's [`datetime` documentation](https://docs.python.org/3/library/datetime.html).\n",
    "Documentation of other useful date utilities can be found in [``dateutil``'s online documentation](http://labix.org/python-dateutil).\n",
    "A related package to be aware of is [`pytz`](http://pytz.sourceforge.net/), which contains tools for working with the most migraine-inducing element of time series data: time zones.\n",
    "\n",
    "The power of `datetime` and `dateutil` lies in their flexibility and easy syntax: you can use these objects and their built-in methods to easily perform nearly any operation you might be interested in.\n",
    "Where they break down is when you wish to work with large arrays of dates and times:\n",
    "just as lists of Python numerical variables are suboptimal compared to NumPy-style typed numerical arrays, lists of Python `datetime` objects are suboptimal compared to typed arrays of encoded dates."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Typed Arrays of Times: NumPy's datetime64\n",
    "\n",
    "NumPy's `datetime64` dtype encodes dates as 64-bit integers, and thus allows arrays of dates to be represented compactly and operated on in an efficient manner.\n",
    "The `datetime64` requires a specific input format:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array('2021-07-04', dtype='datetime64[D]')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "date = np.array('2021-07-04', dtype=np.datetime64)\n",
    "date"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once we have dates in this form, we can quickly do vectorized operations on it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['2021-07-04', '2021-07-05', '2021-07-06', '2021-07-07',\n",
       "       '2021-07-08', '2021-07-09', '2021-07-10', '2021-07-11',\n",
       "       '2021-07-12', '2021-07-13', '2021-07-14', '2021-07-15'],\n",
       "      dtype='datetime64[D]')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + np.arange(12)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because of the uniform type in NumPy `datetime64` arrays, this kind of operation can be accomplished much more quickly than if we were working directly with Python's `datetime` objects, especially as arrays get large\n",
    "(we introduced this type of vectorization in [Computation on NumPy Arrays: Universal Functions](02.03-Computation-on-arrays-ufuncs.ipynb)).\n",
    "\n",
    "One detail of the `datetime64` and related `timedelta64` objects is that they are built on a *fundamental time unit*.\n",
    "Because the `datetime64` object is limited to 64-bit precision, the range of encodable times is $2^{64}$ times this fundamental unit.\n",
    "In other words, `datetime64` imposes a trade-off between *time resolution* and *maximum time span*.\n",
    "\n",
    "For example, if you want a time resolution of 1 nanosecond, you only have enough information to encode a range of $2^{64}$ nanoseconds, or just under 600 years.\n",
    "NumPy will infer the desired unit from the input; for example, here is a day-based `datetime`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2021-07-04')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2021-07-04')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is a minute-based datetime:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2021-07-04T12:00')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2021-07-04 12:00')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can force any desired fundamental unit using one of many format codes; for example, here we'll force a nanosecond-based time:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('2021-07-04T12:59:59.500000000')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64('2021-07-04 12:59:59.50', 'ns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following table, drawn from the NumPy `datetime64` documentation, lists the available format codes along with the relative and absolute time spans that they can encode:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Code  | Meaning     | Time span (relative) | Time span (absolute)   |\n",
    "|------|-------------|----------------------|------------------------|\n",
    "| `Y`  | Year        | ± 9.2e18 years       | [9.2e18 BC, 9.2e18 AD] |\n",
    "| `M`  | Month       | ± 7.6e17 years       | [7.6e17 BC, 7.6e17 AD] |\n",
    "| `W`  | Week        | ± 1.7e17 years       | [1.7e17 BC, 1.7e17 AD] |\n",
    "| `D`  | Day         | ± 2.5e16 years       | [2.5e16 BC, 2.5e16 AD] |\n",
    "| `h`  | Hour        | ± 1.0e15 years       | [1.0e15 BC, 1.0e15 AD] |\n",
    "| `m`  | Minute      | ± 1.7e13 years       | [1.7e13 BC, 1.7e13 AD] |\n",
    "| `s`  | Second      | ± 2.9e12 years       | [ 2.9e9 BC, 2.9e9 AD]  |\n",
    "| `ms` | Millisecond | ± 2.9e9 years        | [ 2.9e6 BC, 2.9e6 AD]  |\n",
    "| `us` | Microsecond | ± 2.9e6 years        | [290301 BC, 294241 AD] |\n",
    "| `ns` | Nanosecond  | ± 292 years          | [ 1678 AD, 2262 AD]    |\n",
    "| `ps` | Picosecond  | ± 106 days           | [ 1969 AD, 1970 AD]    |\n",
    "| `fs` | Femtosecond | ± 2.6 hours          | [ 1969 AD, 1970 AD]    |\n",
    "| `as` | Attosecond  | ± 9.2 seconds        | [ 1969 AD, 1970 AD]    |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the types of data we see in the real world, a useful default is `datetime64[ns]`, as it can encode a useful range of modern dates with a suitably fine precision.\n",
    "\n",
    "Finally, note that while the `datetime64` data type addresses some of the deficiencies of the built-in Python `datetime` type, it lacks many of the convenient methods and functions provided by `datetime` and especially `dateutil`.\n",
    "More information can be found in [NumPy's `datetime64` documentation](http://docs.scipy.org/doc/numpy/reference/arrays.datetime.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dates and Times in Pandas: The Best of Both Worlds\n",
    "\n",
    "Pandas builds upon all the tools just discussed to provide a `Timestamp` object, which combines the ease of use of `datetime` and `dateutil` with the efficient storage and vectorized interface of `numpy.datetime64`.\n",
    "From a group of these `Timestamp` objects, Pandas can construct a `DatetimeIndex` that can be used to index data in a `Series` or `DataFrame`.\n",
    "\n",
    "For example, we can use Pandas tools to repeat the demonstration from earlier.\n",
    "We can parse a flexibly formatted string date and use format codes to output the day of the week, as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-07-04 00:00:00')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "date = pd.to_datetime(\"4th of July, 2021\")\n",
    "date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sunday'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date.strftime('%A')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, we can do NumPy-style vectorized operations directly on this same object:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-07-04', '2021-07-05', '2021-07-06', '2021-07-07',\n",
       "               '2021-07-08', '2021-07-09', '2021-07-10', '2021-07-11',\n",
       "               '2021-07-12', '2021-07-13', '2021-07-14', '2021-07-15'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date + pd.to_timedelta(np.arange(12), 'D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the next section, we will take a closer look at manipulating time series data with the tools provided by Pandas."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Time Series: Indexing by Time\n",
    "\n",
    "The Pandas time series tools really become useful when you begin to index data by timestamps.\n",
    "For example, we can construct a `Series` object that has time-indexed data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-07-04    0\n",
       "2020-08-04    1\n",
       "2021-07-04    2\n",
       "2021-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "index = pd.DatetimeIndex(['2020-07-04', '2020-08-04',\n",
    "                          '2021-07-04', '2021-08-04'])\n",
    "data = pd.Series([0, 1, 2, 3], index=index)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And now that we have this data in a `Series`, we can make use of any of the `Series` indexing patterns we discussed in previous chapters, passing values that can be coerced into dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-07-04    0\n",
       "2020-08-04    1\n",
       "2021-07-04    2\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2020-07-04':'2021-07-04']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are additional special date-only indexing operations, such as passing a year to obtain a slice of all data from that year:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2021-07-04    2\n",
       "2021-08-04    3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['2021']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Later, we will see additional examples of the convenience of dates-as-indices.\n",
    "But first, let's take a closer look at the available time series data structures."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pandas Time Series Data Structures\n",
    "\n",
    "This section will introduce the fundamental Pandas data structures for working with time series data:\n",
    "\n",
    "- For *timestamps*, Pandas provides the `Timestamp` type. As mentioned before, this is essentially a replacement for Python's native `datetime`, but it's based on the more efficient `numpy.datetime64` data type. The associated `Index` structure is `DatetimeIndex`.\n",
    "- For *time periods*, Pandas provides the `Period` type. This encodes a fixed-frequency interval based on `numpy.datetime64`. The associated index structure is `PeriodIndex`.\n",
    "- For *time deltas* or *durations*, Pandas provides the `Timedelta` type. `Timedelta` is a more efficient replacement for Python's native `datetime.timedelta` type, and is based on `numpy.timedelta64`. The associated index structure is `TimedeltaIndex`."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most fundamental of these date/time objects are the `Timestamp` and `DatetimeIndex` objects.\n",
    "While these class objects can be invoked directly, it is more common to use the `pd.to_datetime` function, which can parse a wide variety of formats.\n",
    "Passing a single date to `pd.to_datetime` yields a `Timestamp`; passing a series of dates by default yields a `DatetimeIndex`, as you can see here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2021-07-03', '2021-07-04', '2021-07-06', '2021-07-07',\n",
       "               '2021-07-08'],\n",
       "              dtype='datetime64[ns]', freq=None)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates = pd.to_datetime([datetime(2021, 7, 3), '4th of July, 2021',\n",
    "                       '2021-Jul-6', '07-07-2021', '20210708'])\n",
    "dates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Any `DatetimeIndex` can be converted to a `PeriodIndex` with the `to_period` function, with the addition of a frequency code; here we'll use `'D'` to indicate daily frequency:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2021-07-03', '2021-07-04', '2021-07-06', '2021-07-07',\n",
       "             '2021-07-08'],\n",
       "            dtype='period[D]')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates.to_period('D')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A `TimedeltaIndex` is created, for example, when a date is subtracted from another:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days', '1 days', '3 days', '4 days', '5 days'], dtype='timedelta64[ns]', freq=None)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates - dates[0]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regular Sequences: pd.date_range\n",
    "\n",
    "To make creation of regular date sequences more convenient, Pandas offers a few functions for this purpose: `pd.date_range` for timestamps, `pd.period_range` for periods, and `pd.timedelta_range` for time deltas.\n",
    "We've seen that Python's `range` and NumPy's `np.arange` take a start point, end point, and optional step size and return a sequence.\n",
    "Similarly, `pd.date_range` accepts a start date, an end date, and an optional frequency code to create a regular sequence of dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', '2015-07-10')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, the date range can be specified not with a start and end point, but with a start point and a number of periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03', '2015-07-04', '2015-07-05', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08', '2015-07-09', '2015-07-10'],\n",
       "              dtype='datetime64[ns]', freq='D')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', periods=8)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The spacing can be modified by altering the `freq` argument, which defaults to `D`.\n",
    "For example, here we construct a range of hourly timestamps:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-03 00:00:00', '2015-07-03 01:00:00',\n",
       "               '2015-07-03 02:00:00', '2015-07-03 03:00:00',\n",
       "               '2015-07-03 04:00:00', '2015-07-03 05:00:00',\n",
       "               '2015-07-03 06:00:00', '2015-07-03 07:00:00'],\n",
       "              dtype='datetime64[ns]', freq='H')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('2015-07-03', periods=8, freq='H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To create regular sequences of `Period` or `Timedelta` values, the similar `pd.period_range` and `pd.timedelta_range` functions are useful.\n",
    "Here are some monthly periods:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PeriodIndex(['2015-07', '2015-08', '2015-09', '2015-10', '2015-11', '2015-12',\n",
       "             '2016-01', '2016-02'],\n",
       "            dtype='period[M]')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.period_range('2015-07', periods=8, freq='M')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And a sequence of durations increasing by an hour:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days 00:00:00', '0 days 01:00:00', '0 days 02:00:00',\n",
       "                '0 days 03:00:00', '0 days 04:00:00', '0 days 05:00:00'],\n",
       "               dtype='timedelta64[ns]', freq='H')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.timedelta_range(0, periods=6, freq='H')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of these require an understanding of Pandas frequency codes, which are summarized in the next section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Frequencies and Offsets\n",
    "\n",
    "Fundamental to these Pandas time series tools is the concept of a *frequency* or *date offset*. The following table summarizes the main codes available; as with the `D` (day) and `H` (hour) codes demonstrated in the previous sections, we can use these to specify any desired frequency spacing:"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Code | Description       | Code | Description          |\n",
    "|------|-------------------|------|----------------------|\n",
    "| `D`  | Calendar day      | `B`  | Business day         |\n",
    "| `W`  | Weekly            |      |                      |\n",
    "| `M`  | Month end         | `BM` | Business month end   |\n",
    "| `Q`  | Quarter end       | `BQ` | Business quarter end |\n",
    "| `A`  | Year end          | `BA` | Business year end    |\n",
    "| `H`  | Hours             | `BH` | Business hours       |\n",
    "| `T`  | Minutes           |      |                      |\n",
    "| `S`  | Seconds           |      |                      |\n",
    "| `L`  | Milliseconds       |      |                      |\n",
    "| `U`  | Microseconds      |      |                      |\n",
    "| `N`  | Nanoseconds       |      |                      |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The monthly, quarterly, and annual frequencies are all marked at the end of the specified period.\n",
    "Adding an `S` suffix to any of these causes them to instead be marked at the beginning:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Code  | Description       | Code  | Description            |\n",
    "|-------|-------------------|-------|------------------------|\n",
    "| `MS`  | Month start       |`BMS`  | Business month start   |\n",
    "| `QS`  | Quarter start     |`BQS`  | Business quarter start |\n",
    "| `AS`  | Year start        |`BAS`  | Business year start    |"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Additionally, you can change the month used to mark any quarterly or annual code by adding a three-letter month code as a suffix:\n",
    "\n",
    "- `Q-JAN`, `BQ-FEB`, `QS-MAR`, `BQS-APR`, etc.\n",
    "- `A-JAN`, `BA-FEB`, `AS-MAR`, `BAS-APR`, etc.\n",
    "\n",
    "In the same way, the split point of the weekly frequency can be modified by adding a three-letter weekday code:\n",
    "\n",
    "- `W-SUN`, `W-MON`, `W-TUE`, `W-WED`, etc.\n",
    "\n",
    "On top of this, codes can be combined with numbers to specify other frequencies.\n",
    "For example, for a frequency of 2 hours and 30 minutes, we can combine the hour (`H`) and minute (`T`) codes as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TimedeltaIndex(['0 days 00:00:00', '0 days 02:30:00', '0 days 05:00:00',\n",
       "                '0 days 07:30:00', '0 days 10:00:00', '0 days 12:30:00'],\n",
       "               dtype='timedelta64[ns]', freq='150T')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.timedelta_range(0, periods=6, freq=\"2H30T\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "All of these short codes refer to specific instances of Pandas time series offsets, which can be found in the `pd.tseries.offsets` module.\n",
    "For example, we can create a business day offset directly as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-07-01', '2015-07-02', '2015-07-03', '2015-07-06',\n",
       "               '2015-07-07', '2015-07-08'],\n",
       "              dtype='datetime64[ns]', freq='B')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.tseries.offsets import BDay\n",
    "pd.date_range('2015-07-01', periods=6, freq=BDay())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For more discussion of the use of frequencies and offsets, see the [`DateOffset` section](https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#dateoffset-objects) of the Pandas documentation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Resampling, Shifting, and Windowing\n",
    "\n",
    "The ability to use dates and times as indices to intuitively organize and access data is an important aspect of the Pandas time series tools.\n",
    "The benefits of indexed data in general (automatic alignment during operations, intuitive data slicing and access, etc.) still apply, and Pandas provides several additional time series–specific operations.\n",
    "\n",
    "We will take a look at a few of those here, using some stock price data as an example.\n",
    "Because Pandas was developed largely in a finance context, it includes some very specific tools for financial data.\n",
    "For example, the accompanying `pandas-datareader` package (installable via `pip install pandas-datareader`) knows how to import data from various online sources.\n",
    "Here we will load part of the S&P 500 price history:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018-01-02</th>\n",
       "      <td>2695.889893</td>\n",
       "      <td>2682.360107</td>\n",
       "      <td>2683.729980</td>\n",
       "      <td>2695.810059</td>\n",
       "      <td>3367250000</td>\n",
       "      <td>2695.810059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-03</th>\n",
       "      <td>2714.370117</td>\n",
       "      <td>2697.770020</td>\n",
       "      <td>2697.850098</td>\n",
       "      <td>2713.060059</td>\n",
       "      <td>3538660000</td>\n",
       "      <td>2713.060059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-04</th>\n",
       "      <td>2729.290039</td>\n",
       "      <td>2719.070068</td>\n",
       "      <td>2719.310059</td>\n",
       "      <td>2723.989990</td>\n",
       "      <td>3695260000</td>\n",
       "      <td>2723.989990</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-05</th>\n",
       "      <td>2743.449951</td>\n",
       "      <td>2727.919922</td>\n",
       "      <td>2731.330078</td>\n",
       "      <td>2743.149902</td>\n",
       "      <td>3236620000</td>\n",
       "      <td>2743.149902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018-01-08</th>\n",
       "      <td>2748.510010</td>\n",
       "      <td>2737.600098</td>\n",
       "      <td>2742.669922</td>\n",
       "      <td>2747.709961</td>\n",
       "      <td>3242650000</td>\n",
       "      <td>2747.709961</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   High          Low         Open        Close      Volume  \\\n",
       "Date                                                                         \n",
       "2018-01-02  2695.889893  2682.360107  2683.729980  2695.810059  3367250000   \n",
       "2018-01-03  2714.370117  2697.770020  2697.850098  2713.060059  3538660000   \n",
       "2018-01-04  2729.290039  2719.070068  2719.310059  2723.989990  3695260000   \n",
       "2018-01-05  2743.449951  2727.919922  2731.330078  2743.149902  3236620000   \n",
       "2018-01-08  2748.510010  2737.600098  2742.669922  2747.709961  3242650000   \n",
       "\n",
       "              Adj Close  \n",
       "Date                     \n",
       "2018-01-02  2695.810059  \n",
       "2018-01-03  2713.060059  \n",
       "2018-01-04  2723.989990  \n",
       "2018-01-05  2743.149902  \n",
       "2018-01-08  2747.709961  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas_datareader import data\n",
    "\n",
    "sp500 = data.DataReader('^GSPC', start='2018', end='2022',\n",
    "                        data_source='yahoo')\n",
    "sp500.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For simplicity, we'll use just the closing price:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sp500 = sp500['Close']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can visualize this using the ``plot`` method, after the normal Matplotlib setup boilerplate (see [Part 4](04.00-Introduction-To-Matplotlib.ipynb)); the result is shown in the following figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('seaborn-whitegrid')\n",
    "sp500.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Resampling and Converting Frequencies\n",
    "\n",
    "One common need when dealing with time series data is resampling at a higher or lower frequency.\n",
    "This can be done using the `resample` method, or the much simpler `asfreq` method.\n",
    "The primary difference between the two is that `resample` is fundamentally a *data aggregation*, while `asfreq` is fundamentally a *data selection*.\n",
    "\n",
    "Let's compare what the two return when we downsample the S&P 500 closing price data.\n",
    "Here we will resample the data at the end of business year; the following figure shows the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500.plot(alpha=0.5, style='-')\n",
    "sp500.resample('BA').mean().plot(style=':')\n",
    "sp500.asfreq('BA').plot(style='--');\n",
    "plt.legend(['input', 'resample', 'asfreq'],\n",
    "           loc='upper left');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Notice the difference: at each point, `resample` reports the *average of the previous year*, while `asfreq` reports the *value at the end of the year*."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For upsampling, `resample` and `asfreq` are largely equivalent, though `resample` has many more options available.\n",
    "In this case, the default for both methods is to leave the upsampled points empty; that is, filled with NA values.\n",
    "Like the `pd.fillna` function discussed in [Handling Missing Data](03.04-Missing-Values.ipynb), `asfreq` accepts a `method` argument to specify how values are imputed.\n",
    "Here, we will resample the business day data at a daily frequency (i.e., including weekends); the following figure shows the result:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(2, sharex=True)\n",
    "data = sp500.iloc[:20]\n",
    "\n",
    "data.asfreq('D').plot(ax=ax[0], marker='o')\n",
    "\n",
    "data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')\n",
    "data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')\n",
    "ax[1].legend([\"back-fill\", \"forward-fill\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because the S&P 500 data only exists for business days, the top panel has gaps representing NA values.\n",
    "The bottom panel shows the differences between two strategies for filling the gaps: forward filling and backward filling."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Time Shifts\n",
    "\n",
    "Another common time series–specific operation is shifting of data in time.\n",
    "For this, Pandas provides the `shift` method, which can be used to shift data by a given number of entries.\n",
    "With time series data sampled at a regular frequency, this can give us a way to explore trends over time.\n",
    "\n",
    "For example, here we resample the data to daily values, and shift by 364 to compute the 1-year return on investment for the S&P 500 over time (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sp500 = sp500.asfreq('D', method='pad')\n",
    "\n",
    "ROI = 100 * (sp500.shift(-365) - sp500) / sp500\n",
    "ROI.plot()\n",
    "plt.ylabel('% Return on Investment after 1 year');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The worst one-year return was around March 2019, with the coronavirus-related market crash exactly a year later. As you might expect, the best one-year return was to be found in March 2020, for those with enough foresight or luck to buy low."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rolling Windows\n",
    "\n",
    "Calculating rolling statistics is a third type of time series–specific operation implemented by Pandas.\n",
    "This can be accomplished via the `rolling` attribute of `Series` and `DataFrame` objects, which returns a view similar to what we saw with the `groupby` operation (see [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb)).\n",
    "This rolling view makes available a number of aggregation operations by default.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "For example, we can look at the one-year centered rolling mean and standard deviation of the  stock prices (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rolling = sp500.rolling(365, center=True)\n",
    "\n",
    "data = pd.DataFrame({'input': sp500,\n",
    "                     'one-year rolling_mean': rolling.mean(),\n",
    "                     'one-year rolling_median': rolling.median()})\n",
    "ax = data.plot(style=['-', '--', ':'])\n",
    "ax.lines[0].set_alpha(0.3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As with `groupby` operations, the `aggregate` and `apply` methods can be used for custom rolling computations."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Where to Learn More\n",
    "\n",
    "This chapter has provided only a brief summary of some of the most essential features of time series tools provided by Pandas; for a more complete discussion, you can refer to the [\"Time Series/Date Functionality\" section](http://pandas.pydata.org/pandas-docs/stable/timeseries.html) of the Pandas online documentation.\n",
    "\n",
    "Another excellent resource is the book [*Python for Data Analysis*](https://learning.oreilly.com/library/view/python-for-data/9781098104023/) by Wes McKinney (O'Reilly).\n",
    "It is an invaluable resource on the use of Pandas.\n",
    "In particular, this book emphasizes time series tools in the context of business and finance, and focuses much more on particular details of business calendars, time zones, and related topics.\n",
    "\n",
    "As always, you can also use the IPython help functionality to explore and try out further options available to the functions and methods discussed here. I find this often is the best way to learn a new Python tool."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example: Visualizing Seattle Bicycle Counts\n",
    "\n",
    "As a more involved example of working with time series data, let's take a look at bicycle counts on Seattle's [Fremont Bridge](http://www.openstreetmap.org/#map=17/47.64813/-122.34965).\n",
    "This data comes from an automated bicycle counter installed in late 2012, which has inductive sensors on the east and west sidewalks of the bridge.\n",
    "The hourly bicycle counts can be downloaded from [http://data.seattle.gov](http://data.seattle.gov); the Fremont Bridge Bicycle Counter dataset is available under the Transportation category.\n",
    "\n",
    "The CSV used for this book can be downloaded as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "# url = ('https://raw.githubusercontent.com/jakevdp/'\n",
    "#        'bicycle-data/main/FremontBridge.csv')\n",
    "# !curl -O {url}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Once this dataset is downloaded, we can use Pandas to read the CSV output into a `DataFrame`.\n",
    "We will specify that we want the `Date` column as an index, and we want these dates to be automatically parsed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fremont Bridge Total</th>\n",
       "      <th>Fremont Bridge East Sidewalk</th>\n",
       "      <th>Fremont Bridge West Sidewalk</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01 00:00:00</th>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 01:00:00</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 02:00:00</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 03:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-01 04:00:00</th>\n",
       "      <td>6.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     Fremont Bridge Total  Fremont Bridge East Sidewalk  \\\n",
       "Date                                                                      \n",
       "2019-11-01 00:00:00                  12.0                           7.0   \n",
       "2019-11-01 01:00:00                   7.0                           0.0   \n",
       "2019-11-01 02:00:00                   1.0                           0.0   \n",
       "2019-11-01 03:00:00                   6.0                           6.0   \n",
       "2019-11-01 04:00:00                   6.0                           5.0   \n",
       "\n",
       "                     Fremont Bridge West Sidewalk  \n",
       "Date                                               \n",
       "2019-11-01 00:00:00                           5.0  \n",
       "2019-11-01 01:00:00                           7.0  \n",
       "2019-11-01 02:00:00                           1.0  \n",
       "2019-11-01 03:00:00                           0.0  \n",
       "2019-11-01 04:00:00                           1.0  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('FremontBridge.csv', index_col='Date', parse_dates=True)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For convenience, we'll shorten the column names:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "data.columns = ['Total', 'East', 'West']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's take a look at the summary statistics for this data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Total</th>\n",
       "      <th>East</th>\n",
       "      <th>West</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>147255.000000</td>\n",
       "      <td>147255.000000</td>\n",
       "      <td>147255.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>110.341462</td>\n",
       "      <td>50.077763</td>\n",
       "      <td>60.263699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>140.422051</td>\n",
       "      <td>64.634038</td>\n",
       "      <td>87.252147</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>14.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>7.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>60.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>145.000000</td>\n",
       "      <td>68.000000</td>\n",
       "      <td>74.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1097.000000</td>\n",
       "      <td>698.000000</td>\n",
       "      <td>850.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Total           East           West\n",
       "count  147255.000000  147255.000000  147255.000000\n",
       "mean      110.341462      50.077763      60.263699\n",
       "std       140.422051      64.634038      87.252147\n",
       "min         0.000000       0.000000       0.000000\n",
       "25%        14.000000       6.000000       7.000000\n",
       "50%        60.000000      28.000000      30.000000\n",
       "75%       145.000000      68.000000      74.000000\n",
       "max      1097.000000     698.000000     850.000000"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna().describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the Data\n",
    "\n",
    "We can gain some insight into the dataset by visualizing it.\n",
    "Let's start by plotting the raw data (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data.plot()\n",
    "plt.ylabel('Hourly Bicycle Count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The ~150,000 hourly samples are far too dense for us to make much sense of.\n",
    "We can gain more insight by resampling the data to a coarser grid.\n",
    "Let's resample by week (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "weekly = data.resample('W').sum()\n",
    "weekly.plot(style=['-', ':', '--'])\n",
    "plt.ylabel('Weekly bicycle count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This reveals some trends: as you might expect, people bicycle more in the summer than in the winter, and even within a particular season the bicycle use varies from week to week (likely dependent on weather; see [In Depth: Linear Regression](05.06-Linear-Regression.ipynb), where we explore this further). Further, the effect of the COVID-19 pandemic on commuting patterns is quite clear, starting in early 2020.\n",
    "\n",
    "Another option that comes in handy for aggregating the data is to use a rolling mean, utilizing the `pd.rolling_mean` function.\n",
    "Here we'll examine the 30-day rolling mean of our data, making sure to center the window (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily = data.resample('D').sum()\n",
    "daily.rolling(30, center=True).sum().plot(style=['-', ':', '--'])\n",
    "plt.ylabel('mean hourly count');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The jaggedness of the result is due to the hard cutoff of the window.\n",
    "We can get a smoother version of a rolling mean using a window function—for example, a Gaussian window, as shown in the following figure.\n",
    "The following code specifies both the width of the window (here, 50 days) and the width of the Gaussian window (here, 10 days):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "daily.rolling(50, center=True,\n",
    "              win_type='gaussian').sum(std=10).plot(style=['-', ':', '--']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Digging into the Data\n",
    "\n",
    "While these smoothed data views are useful to get an idea of the general trend in the data, they hide much of the structure.\n",
    "For example, we might want to look at the average traffic as a function of the time of day.\n",
    "We can do this using the `groupby` functionality discussed in [Aggregation and Grouping](03.08-Aggregation-and-Grouping.ipynb) (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_time = data.groupby(data.index.time).mean()\n",
    "hourly_ticks = 4 * 60 * 60 * np.arange(6)\n",
    "by_time.plot(xticks=hourly_ticks, style=['-', ':', '--']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The hourly traffic is a strongly bimodal sequence, with peaks around 8:00 a.m. and 5:00 p.m.\n",
    "This is likely evidence of a strong component of commuter traffic crossing the bridge.\n",
    "There is a directional component as well: according to the data, the east sidewalk is used more during the a.m. commute, and the west sidewalk is used more during the p.m. commute.\n",
    "\n",
    "We also might be curious about how things change based on the day of the week. Again, we can do this with a simple `groupby` (see the following figure):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Eb3piMyAggFmzZvHaa68BMGPGDLy8vADQ6/VYW1tz8OBB2rZti06nQ6fTERAQwOHDhwkPD6+2QoWoiwI97Hn/Hy0Z368Ji3ak8vX2VB75cifNfZ14qmcQ94T7YqWRo55/99iqx656LDIwkoebPUxhWSHPrnv2quf/EfIP7g+5n/NF53lp00sVnvtq4Fc3/H5du3bls88+4/HHH2fz5s0MHTqUadOmkZeXR3x8PB07dmTVqlUsXLgQtVpNREQEr7zyCnFxcXz44YdotVr0ej3z589nzpw5JCUlMXv2bJ5//vmq/SC4hRCPjIwkLS3N1L4c4Hv37mXRokV88803/PHHHzg6Opq2sbe3Jz8//5qvd6t/clxLUVFRlfavLepKP0D6Up36+UHP+33ZeDyf7+JzGLdsP//++U/+EebMoCZO2OtuPczN3Zfqcr1+XLx48arH0tPTSVQSKdYXX/P5v878RWJpIrmluVc9f7OflUql4vDhwyQkJPDHH39w991307RpU5YvX05qaiqBgYFMmzaN6dOnY21tzccff8ySJUvYv38/7dq1Y/DgwWzbto29e/fSr18/Dhw4QN++favld1SpKYa//fYbn3/+OXPnzsXNzQ0HBwcKCgpMzxcUFFQI9SuFhYVVrlKMP+iq7F9b1JV+gPSlJrRuCWPvVdh0NJN5W04wPy6bZYdyGd6xIY91a4yfi+1NX6O29KWqrtePZWHLbrjfspY3eT78xs9fS3h4OFlZWTRs2JDw8HAKCgrYtGkTqampPPTQQyxcuJDp06cDmPLwzTffZM6cOUyZMgVbW1vuv/9+SkpKsLW1va3fT1xc3HWfu+2/03788UcWLVpETEwMDRs2NHUuLi6O4uJi8vLySE5OpkmTJrf70kKIS9RqFX2aebPkqc78/Hx3+jTzYsG2FHp8tJEXl+7j0Okcc5dY73Tr1o0vvviCHj16ABAREUFCQgIGgwF/f398fX1ZsGABMTExjBw5kjZt2vDTTz/xwAMPmPJy+fLlqNVqDAZDtdV1WyNxvV7PBx98gK+vLy+88AIAHTp0YOzYsURHRzNixAgURWH8+PFYW1tXW5FC1Get/J2ZObwtEwY146utJ1iy6yQ/7j9D12B3nuwRRK8mnnIHxTuga9euTJw4kY8++ggAnU6Ho6MjYWFhuLm58eijjxIdHY1er6dBgwYMGjSIkpISJk6ciK2tLYWFhUyfPh13d3dKS0uZOnUqr776apXrUil38DR4XFwcERERld6/rv+JaImkL3deTmEpS3ed5KttKaTnFhHq5cCTPYL4R1s/rLXGOyhaSl9upq70A6rWlxtlp5z2FsLCONta8XSvYLa8dhcfR7VGq1Hz2ncH6TZlI7M3HOO83EGxXpEQF8JC6bRqHmjrz29ju/PNPzvRws+JaWuO0u3DDSRkylWg9YWEuBAWTqVS0S3Eg68f78jqcT1xsrFi7u5suWConpAQF6IOaerjyLh+oRw5W8zahAxzlyPuAAlxIeqYhyL8aeBkxdTVR9AbZDRe10mIC1HHaDVqRrV15VhmPj/sO23uckQNkxAXog7q3sielg2c+HjdUYrL9OYuR9QgCXEh6iC1SsWrkc1IO1/Ikp0nzV2OqEES4kLUUT1DPegc5MbsjUmyilAdJiEuRB2lUql4bWAzzuaX8NW2E+YuR9QQCXEh6rB2Aa70C/Pmi83H5UrOOkpCXIg67tXIpuSXlDFnc7K5SxE1QEJciDquqY8jD7RpwMLYFNJz5HL8ukZCXIh6YHz/JhgUhU/WHzN3KaKaSYgLUQ80dLNjRMcAlu85xYmzBTffQVgMCXEh6onn+4Si06iZvuaIuUsR1UhCXIh6wtPRmse7B/LLwb9kebc6REJciHrkqZ7BONtaMU1G43WGhLgQ9YizrRVjegez6UgWO49nm7scUQ0kxIWoZ0Z3CcTbyZqPVh+RhSPqAAlxIeoZW52GsX1DiUs9z4bDmeYuR1SRhLgQ9dCw9g0JdLdj6uojGGThCIsmIS5EPWSlUfPSgKYcTs/jpwNnzF2OqAIJcSHqqXtb+dLc14npa49QUmYwdzmikiTEhain1GoVr0Y25dS5QpbtloUjLJWEuBD1WO+mnnQMdGPmhiQulsjCEZZIQlyIesy4cERTsvKK+WpbirnLEZUgIS5EPdc+0I2+zbz4YnMyORdLzV2OuE0S4kIIXolsSl5xGXO2yMIRluaWQvzAgQNER0cDkJqayvDhwxkxYgTvvPMOBoPxrPbs2bN56KGHePjhhzl48GDNVSyEqHZhvk7c19qPr7adIDNXFo6wJDcN8Xnz5jFx4kSKi4sBmDx5MuPGjWPx4sUoisL69euJj49n165drFixghkzZvDee+/VeOFCiOr1Uv8mlOkVZm6QhSMsyU1DPCAggFmzZpna8fHxdOzYEYCePXsSGxtLXFwc3bt3R6VS4efnh16v59y5czVXtRCi2jVyt+fhjg1ZuusUqdmycISluGmIR0ZGotVqTW1FUVCpVADY29uTl5dHfn4+Dg4Opm0uPy6EsCxj+4Si1aiYsfaouUsRt0h7800qUqvLc7+goAAnJyccHBwoKCio8Lijo+M1909MTKxEmUZFRUVV2r+2qCv9AOlLbVWVvtzX1IkV+88woKGKIDfraq7s9sjv5OZuO8SbN2/Ozp076dSpE1u2bKFz584EBAQwdepUnnjiCdLT0zEYDLi5uV1z/7CwsEoXm5iYWKX9a4u60g+QvtRWVenLW41KWZW0ge+OlbLg0TbVW9htkt+JUVxc3HWfu+0QnzBhApMmTWLGjBkEBQURGRmJRqOhffv2REVFYTAYePvttytVqBDC/JztrHi6VzBTVx9hT8o52gdee0AmaodbCnF/f3+WL18OQOPGjVm0aNFV27zwwgu88MIL1VudEMIsHusWyMLYFD5cdZjlT3cxnQcTtY9c7COEuIqdTsvYPiHsTjnPpiNZ5i5H3ICEuBDimqI6BBDgZsdHsnBErSYhLoS4Jp1WzUv9m5D4Vy6//PmXucsR1yEhLoS4rvta+9HMx5Hpa45QqpeFI2ojCXEhxHVdXjgiNfsiy/ecMnc54hokxIUQN9SnmRcRjVyZuf4YhSV6c5cj/kZCXAhxQyqVitcim5KRW8zX21PMXY74GwlxIcRNdQpyp3dTTz7flExOoSwcUZtIiAshbskrA5qSU1jKXFk4olaREBdC3JKWDZy5N9yXBVtTyMyThSNqCwlxIcQte3lAU0r0Bj7dkGTuUsQlEuJCiFvW2MOeYe0bsnjXSU6du2jucgQS4kKI2/Ri31DUKhUfy8IRtYKEuBDitvg42/Bo10C+33+aI+mygpe5SYgLIW7bM72CcdBpmbr6iLlLqfckxIUQt83VXsfTvYJYl5hBXOp5c5dTr0mICyEq5bFujfFw0PHRqsMoityq1lwkxIUQlWJvreX5u0LYeeIcW46dNXc59ZaEuBCi0oZ3CsDf1Zapqw/LwhFmIiEuhKg0a62G8f2acOh0Lr8dkoUjzEFCXAhRJfe3bUATbwdmrDlKmSwcccdJiAshqkSjVvHKgKYcP1vAt3Fp5i6n3pEQF0JUWf/m3rQNcOG/645RVCoLR9xJEuJCiCozLhzRjPTcImK2p5q7nHpFQlwIUS26BLvTI9SDzzYlkVskC0fcKRLiQohq81pkM85fLOXLLcfNXUq9ISEuhKg2rfyduaeVL19uPcHZ/GJzl1MvSIgLIarVSwOaUFxmYLYsHHFHSIgLIapVsKcDD7XzZ/HOk6Sdl4UjapqEuBCi2r3YLxRU8N91x8xdSp2nrcxOpaWlvP7665w+fRq1Ws2//vUvtFotr7/+OiqVitDQUN555x3UavmMEKI+8nOxZVTnRizYdoKnewYR6u1o7pLqrEql7ObNmykrK2Pp0qU899xz/Pe//2Xy5MmMGzeOxYsXoygK69evr+5ahRAW5Nm7QrDTaZm2RhaOqEmVCvHGjRuj1+sxGAzk5+ej1WqJj4+nY8eOAPTs2ZPY2NhqLVQIYVnc7HU82SOI1fEZ7D91wdzl1FmVOpxiZ2fH6dOnGTRoEOfPn2fOnDns3r0blUoFgL29PXl51157LzExsdLFFhUVVWn/2qKu9AOkL7VVbelLdy8DC2zUvPNdHFMi/W57/9rSj+pQU32pVIgvXLiQ7t278/LLL/PXX38xevRoSkvLr9AqKCjAycnpmvuGhYVVrlKMHwBV2b+2qCv9AOlLbVWb+vJirh3v/5JAttaT7qEet7VvbepHVVWlL3Fxcdd9rlKHU5ycnHB0NJ6ocHZ2pqysjObNm7Nz504AtmzZQvv27Svz0kKIOuaRzgE0cLHlo9WyjFtNqFSIP/roo8THxzNixAhGjx7N+PHjefvtt5k1axZRUVGUlpYSGRlZ3bUKISyQtVbDi/1COZiWw6pD6eYup86p1OEUe3t7Pvnkk6seX7RoUZULEkLUPUPaNmDuluNMW3OE/s290Wpk+nF1kZ+kEKLGaTVqXhnQhOSsAlbuPW3ucuoUCXEhxB0R2cKH1v7O/HfdUVk4ohpV6nCKqJwyQxkn806y/8J+zp0+R2vP1jjoHDhbeJbT+afRqrRo1Bo0Kg0atYaGDg2x0lhRUFrAxdKLpue0ai0alQZrjbVpWqcQtZ1KpeK1gc145MudfLPzJE90b2zukuoECfEaoDfoOZ1/GlcbVxx1juz4awdTd0/lRM4JSg2XpmIeheX3LifMPYwNJzfwrx3/uup1fnngFxo5NWLFkRVMj5t+1fMbh23Ew9aDz/d/ztw/51b4ENCqtfw+5HfsrOyYe3AuPyb9eNWHwJJ7lqBSqfg6/mu2nt5q+vDQqDTYW9kzucdkAJYdXkbCuQQ0Kg1qlRqtWouzzpkxbcYAcPjcYbQqLQFOAeg0upr7wQqL1y3Eg24h7ny6MYmoDg1xsJYIqir5CVaD7MJsfkj6geQLySRdSOJ4znGK9cVM7TWVgYEDcbRyxNvOm25+3QhxDcGQbSAwMJBGTo0A6Onfk8/7fY7eoKdMKUNv0KNX9HjYGufUdvHrwiSrSegVvem5MkMZdlo7ANp4tWF089HG56/YxkptBYCvvS8tPFpgUAym76EoimkUX2Yoo6isqML+1lprU/+OXTjG1tNbTa+rV/S427ibQnzq7qnsSt+FWqWmgUMDGjs3pq1XW/7Z6p8AFJQWYKe1k78aBGBcOOIfn27jyz+OM65fE3OXY/FUyh2cuBkXF0dERESl9zfXxH9FUci4mGEK6aQLSSRfSOb+kPsZ1nQYaXlpDFo5CC87L0JcQgh2CSbUJZTOvp3xdfCtNf2oCYmJiWh8NBw7f4yU3BRO5JwgJScFPwc/ZvaZCcDg7wdzrugcgc6BNHZqTKBzIG292hLhXfn3Qk2oa7+X2tyXZ2Li2Jp0ls2v9sbdwfq629X2ftyOql7sc73slJH4FRRFIbso2xTSnraeDAgcQImhhMjvIjEoBgDcbNwIdQnF3soeAD8HP7YN34aT7tpXqdZ1TVyb0MT1+iOqEWEjSL6QzImcE2w/s50fk3/kgZAHiPCOwKAYGPbzMHztfWns3JjGzsaQD3YJrrc/z/rglcgmrElI57NNyUy6t7m5y7Fo9TbEzxed53zReYJcggAYv3E8ezL2cKH4gmmb/o36MyBwANYaa/7V7V/42vsS4hKCq41rhddSq9QSODcwvNnwCu38knyK9EUAFJYVEuAUwImcE2w7s810zmBM6zE82+ZZcopzmL5neoVRvL+jv+lQkbBMIV6OPNjOn5gdqTzRvTF+LrbmLsli1ZsQX5+6nl3pu0yHQ84VnSPUNZSV960EwMvOi36N+pkOh4S4hOBu427a/77g+8xVep3joHPAAQcA7K3smdF7BmA8IXym4Awnck7g7+gPQObFTLakbeH7pO9N+2tVWib3mMzAxgPJKMgg9kysaRTvbO185zskKmVc/yb8uP8Mn6w7xocPhZu7HItVZ0K8oLTgqmPWmRczWXnfSlQqFWtPrmXjyY0EuwTT078nIS4hNHVratr/jU5vmLF6ARinVTo2pKFjQ9Njoa6hbIraRG5JLik5l46556YQ4hICwL6sfbwd+7ZpezcbNwKdApnYeSKhrqFkF2ZTUFqAn4MfWnWdebvXCQ1cbHmkcwBfx6bwZM8gQrwczF2SRbK4d/XF0oucyDlhCusxrceYptEtOLQAABuNDUEuQTR3b06xvhgbrQ1vd36b/3T/D2qVXN9kiZx0ToR7hhPuWXHE1i+gH7888EuFgD+Rc8J0vuKX478wbc80rNRWBDgGmI65P97ycRx1jugNejRqjTm6JIDn7gph+e5TzFh7hM8eqV0nui2FxYT45lOb+deBf5G5KxMF44QaK7UV9wXfR6hrKPcE3UMbzzaEuITg5+B31f+YdlZ25ihb1DCtWksjp0Y0cmpEr4a9rnq+d8PeOOmcOJF7wvThvyVtC8+0fgaA6XHT+e34bzR2boyPyoeBDgNp590OR50sJ3YneDhY80SPIGauP8bBtAuE+7uYuySLYzEh7mbjRpB9EA+GPUiISwghLiE0dGxo+hP5ZjMkRP10OeCvVGYoM71v2ni2Ia8kjxM5J1h1dhW/pP+Cu407G4dtRKVSceTcEXwdfOXEdQ16skdjYranMHX1EWKe6GTuciyOxYR4K89WjA8ZX2fmjArzufLY+IDAAQwIHADAgfgDlLiXcLbwrOnCpJc2vURafhrN3JrR3rs9HXw60M67nYR6NXK0seK5u0L496+JxCadpWvI7S0cUd/JAWIhLtGpdXTw6cCgxoMA43UD73Z9l2fCn8Heyp6lh5fywoYX+GjXR6bnt6RtIbck15xl1wkjOzfC19mGD1cfkYUjbpPFjMSFuNNUKhUdfDrQwacDYxhDsb6Yg1kHTcfLT+Se4Ln1z6FCZRyp+7Q3jdblmPrtsbHSMK5fKBO++5M1CRlEtvAxd0kWQ0biQtwia401HXw60MytGQD+Dv58FfkVY9qMwVHnyLLDy3hx44vsSt8FQFpeGhtObiCnOMecZVuMB9v5E+Rpz7TVR9AbZDR+q2QkLkQl6TQ64+jbpz20hmJ9MX9m/WkK+TWpa/g47mNUqGjq1tQ0Su/eoLvc7fEatBo1L/dvynOL9/L9vtM8FOFv7pIsgozEhagm1hpr2vu0x0FnvGhlZNhIFg5cyLNtnsVZ58yKoyt4ZfMr6BXjgghb0rbISP1vBrX0oVUDZz5ee5TiMlk44lbISFyIGqLT6IjwjjDerbE1lOhLOJ5zHFut8T4hC+MXsjt9NypUNHFtQgefDnT160oP/x5mrtx81GoVr0Y2ZdSCXSzZeZJObuauqPaTEBfiDtFpdKZDLQBz+s3h0NlD7E7fze6M3Xx79FtO5p00hfi8g/MIcg4iwjsCFxsXM1V95/UI9aBzkBuzNiQR/g8/c5dT60mIC2EmOo2Odt7taOfdjqd5mlJ9qekumgWlBcz7cx6FZYWA8WK29t7tuTfoXlp5tjJj1TXv8jJuQz6L5YeEHNrJvbFuSEJciFrCSmOFp50nYLy747aHt3Eo+9JIPX03K4+tJMQ1hFaerfgr/y8Wxi+kg08HIrwjrro9sqVrF+BK/+beLDqQwYbUDfg42eDtbIO3ow0+ztZ4O9ng7WRjfNzJBltd/b3/jYS4ELWUlcaKtl5taevVlqfCn6JUX2o6KZp0IYmVx1ay+PBiwHi3xw7eHXis5WP42NeNOdb/eaAVU9XFFGvtyMgtIuFMLhtyMiksvfqEp5ONFh9nm7+FuzHsLz/u4WCNRl33lgiUEBfCQlhprLDCuBhGD/8exA6PJT473jRS/z7pe54MfxLAtAqVJfN0tOaxCLcKt9pQFIW84jIycopIzy0iI7eYjNwiMnKLSM8x/vdYRj6ZeUX8faq5Rq3C08H6qnCvEPrONjhaay1qPVgJcSEslJXGijZebWjj1YYnw5+k1FCKldqK9IJ03oh/g7dc36KTb926oZRKpcLJxgonGytCva9/VazeoHA2v7hCuGfkFl8K/iJSsgvYcTyb3KKyq/a102kuhbt1hUM2xuA3PublaINOWztmaEuIC1FHXF6yzt7KnlKllPGbxrPo7kUEOQeZubI7T6NWmYI3/AbXDBWW6I1Bn1t0xYi+mIy8IjJyith78jwZOcWU6K/+y8bdXmcKex9nY7D7OBtD38vJGh8nG9zsdTU+qpcQF6KOcdQ58nqT13n7yNs8u+5ZFt+zGDcbmXB9LbY6DYEe9gR62F93G0VROH+xtDzsc8pH9ZmXHvvzdA5n80uu2lenUePpaAz53g211MRNWCXEhaiDvKy9mNVnFo+vfpyxG8YyP3I+1hprc5dlkVQqFW72OtzsdYT5Xv8WxCVlBrLyi0nPKQ/3y8fs03OKKCqtmfMUEuJC1FHhnuFM7jGZuQfnkleSh7WthHhN0mnVNHCxpYGL7TWfT0xMrJHvW+kQ/+KLL9iwYQOlpaUMHz6cjh078vrrr6NSqQgNDeWdd95Bra4dB/6FqK/6N+rPXQ3vQqvWoiiKRc26ELemUim7c+dO9u3bx5IlS4iJiSE9PZ3Jkyczbtw4Fi9ejKIorF+/vrprtVwGA1w4BcV5AGgL/oK1b8PZJOPz51Nh04fG/15ub5sJuX+Vt3d/CQVnje0LJ2H/Eig8f6l9CuJ/ML0+Oafh2FooNV7tR+5fkLINyoqN7fxMOLMP9JfOzF88B1lHwXBp/m1RjvE1L9+cv6TAuM3ldllJ+WuLWk+r1lJYVsi4jeP4/tj35i5HVLNKhfjWrVtp0qQJzz33HM888wy9e/cmPj6ejh07AtCzZ09iY2OrtVCLYLh0zKsgG9a/D6f3GtvpB+C/LeH4JgCsCs/CjjmQc8r4/IVU2PSf8vbZY7B2EuSkGdsZ8fDry+Xt03HwwzPlIX9yO6wYDXkZxnbyBvjmISjIMraP/AYL7y4P/UMrYW5vKL60Is2+GPi0Q3kw75pnrFdfamxv+wQ+agyXR3Gb/gNTAsr7ve0TWDCwPOSPb4L9i8ufLyko/4AQZnE5yN/f/j47/9pp7nJENVIplVgLaeLEiZw5c4Y5c+aQlpbGmDFjKCgoYOvWrQBs376d7777jmnTplXYLy4uDju7yq86X1RUhI2NTaX3rzaGMhzPbKXE3pdi16aoS/II/m0oZ5s/xvkmUaiLc2jy492kR7zKheD7UZUV4pyyigLfTpTa+13dD0UBxWAMSZUaDGWo9cUYNNag1qLSl6AuyUNv7Wxsl15EW3yOUltv0FihLsnD6mIGJY4BKBodmqJzWOWfodi1CYpGh/ZiJrq8kxR6hKNodFjln8E6J5l83y6g1qLLTcHmwjFy/e8CtRbr88ewOX+YnMb3gEqNTfYhbLMTON9kGAB2mXuxOZfIuWaPUFRUhM/p1dhlxnGmy/sA+O54D7usfSQP/gEAvx3vYpt9iOR7vgXA7fA3aErzyWr1NAA25w6jqK0odgm+c7/Da6g1769qcK2+XCy7yKTESZwrOce/m/+bBrYNzFTdravrv5NbdfHiRSIiIq79pFIJU6dOVebPn29qDx48WAkPDze1165dq7z33ntX7bdnz57KfDuThISEKu1/Wy6kKUr28fL2khGK8sfHxq/1ekX5t4+i/P66sW0wKMqvryhK8sby7ctKrvvSd7QfNeyafSktUpTc9PJ24q+Ksrv8/aL8+LyifBNV3v7qHkWZH1neXjpSUX54rry9Z6HxNS7Lz7rhz7ey6vzvRVGUtLw0pefSnkrkt5FKdmH2Ha7q9tWH38mtuFF2VupwSkREBH/88QeKopCRkUFhYSFdunRh507jn2lbtmyhffv2lfrEuaNKi8q/3vG58d9l/7sP1r1T3lZrjaNkALUantwAd71pbKtUcPdUCOpdvr3GqsbKrvW01uDoXd5udje0f7y8fd8sGLG0vD1wCkR+UN72bAbuV4zKY2fCoW/L21/2he+fKW9/Pwb2fFXePrYWspOr3o86qIFDA2b3mY1BMZBekG7uckQ1qNTslLvuuovdu3fz0EMPoSgKb7/9Nv7+/kyaNIkZM2YQFBREZGRkdddaNanbjScEW0cZ20sfgbx0ePLSCdjkjcag7jzG2B44BeyuuEBi2NcVX8+rBmbt11c+LSu2+7xVsf3sTii74gO352vgeMVNns6nlIe+osDSEdDpGRjwL2N7elPoOha6Pm88b7HhfWgyCAI6XTrpnAKO9ee+1a08W/HrA79idWmgocisFYtW6SmGr7322lWPLVq0qErFVEnBWeMJwUZdjO3YWZD4Mzyxxtg+sBgO/1oe4s3/YZyFcdmIZeUn7gBC+9+ZusXNabSgcShvt32k4vOP/16x/c/1YONs/LqsGMIGg3uIsV10AWJng6OvMcQvnoWZbWHQVHDqAcX5sOsLaH5/xb8G6hgrjRWKovDp/k8xKAbGthtr7pJEJVnURG51SX75DJDjm2DFY8bpbmCcgvfVoPIZFrZu4OxfPiuizyQYu6/8xcKHQccny9syEqkbVCrwDQfXRsa2lQ3cMx2aDjS27dxgYmb54R0rW/jHZ+WHwrIOG2cWZV+a/pl1BJaPNv63Dsouymben/Nk6qEFs5wQP7CMpt/3M07HA8jPMs51vjyNrtVQGPWj8ZAIGEdrDy0A9aWbxTt4lY/ORP2mVpefs7B2NL5XPJsY2/7tYUIqNO5lbOelG99nqkvvo4QfjSP3y3P6C8+Xz8+3MCqVijc7vUkX3y4y9dCCWU6IN4ggI/w50F26UU34UHhxPzhfmiblHgxBver3CUVRPWxdjCN4ML6nxh0Ej0uHY2xdwbtl+TH53fONc+YvB3n6n8bzL7c/c9csrNRWTO89nUDnQMZvHM/xC8fNXZK4TZYT4h4hnAuLNo6ohTCXxj0hKsY4AwcgpC9ETjaO6ME4w2n5qPLtDy6HuIV3vMzb4ahz5NO+n2Kvs+fohaPmLkfcJrkBlhBV4dfW+O+yfu8aj7dfPsfy57fGK2MjHjW2f3sV7Dyg9wRj22AwHt4xMz8HP36+/2dstHXjwpr6xPzvHiHqEgcv43H1y0Ysg0dWlLcLz5ff7gDgs06wZmJ5O/Nw+cn6O+xygK9JWcNbW9+qE0u81QcS4kLUJJWq/FALwINfll/YZDBAs3vBr52xXVoEc7rBpsnlz++NMd6M7A46k3+Gn5J/Yta+WXf0+4rKkRAXwlzUauj3DrQcUv7YkHnQ8kHj19lJ8NPzcGKzsZ2fCb++Yhyt16DRLUYztMlQvvzzS5l6aAEkxIWoLaxsjIF++QpW9xB4Pg6a3m1snzsOB5YYL1gCOLkD5keWz2EvK66Wu0VennrYza8b729/nx1/7ajya4qaIyEuRG2lVhunNl6+/UNAZ3j9JPh3MLb1l46d2156/sAS43TH3DNV/tZatZZpvaYR6BzIrr92Vfn1RM2R2SlCWJLLF6+BcbrjEz3L255h0G6U8ZYC5MBfB8CrhfG2BZXgoHNg0d2LsLe6/iLCwvxkJC5EXRHQCQZOBpUKTWE2LBhkXFykCi4H+OFzh3lp00sUXXkjMlEryEhciDpIb+sO982EgEs3hNOXVXpEDnA67zTrUtehQsXUXlNRq2T8V1vIb0KIuqrVQ+W3pfjxOfh5XKVvB9C3UV9einiJNalrmLl3ZvXVKKpMRuJC1HUGAzj5gdamSnfrHN1iNCfzTjL/0HwaOjbkwSYPVmORorJkJC5EXXd5PvrlS/3P7IOdX9z2qPzKqYfrT65HsZCbfNV1MhIXor7Zf3mBlIdv+/bMWrWWGb1nYKWxktWAagkZiQtR3wz6CJ5Yawxwg8E4Mr8NdlZ2WKmtyC7M5tXNr3K28GwNFSpuhYS4EPWNSlV+wnP/Iph7F5zafdsvk34xnU2nNvHihhdl6qEZSYgLUZ+1fBDunlp+50V92S3v2sK9BVN6TOHPs3/y5tY35a6HZiIhLkR9prM3rjWrUhkXG/+sMxz5/eb7XdK3UV9ebv8ya1PX8sneT2qwUHE9EuJCCCN9iXEqorP/be02qvkohjUZxrrUdeSX5NdQceJ6ZHaKEMLIyQ9G/1Te3vmF8WZbDdrdcDeVSsUbnd6goLQAB51DDRcp/k5G4kKIq5UUQOzsW14fVKvW4mztTKm+lA92fEDS+aSarU+YyEhcCHE1nT08vbl8QeicNFBbgaP3DXc7V3SOdSfXsSVtC9/c8w0eth53oNj6TUbiQohrs3MzhjkY773y1aCbzl7xtvdmdp/ZnCs6x9gNYyksK7wDhdZvEuJCiJsbOMX47/KdEG8Q5i08WjCl5xQOnT0kCy7fARLiQoib8wqDJgOMX8f/APPugrz0627eN8A49TD2TCwpuSl3pMT6So6JCyFuj5UtOPqAnfsNNxvVfBQDAwfibX/j4+iiaqo0Es/OzqZXr14kJyeTmprK8OHDGTFiBO+88w4Gg/wJJUSd1CQSHlkBGisouQhrJkFRzlWbqVQqvO29URSFmIQYYs/EmqHYuq/SIV5aWsrbb7+NjY0NAJMnT2bcuHEsXrwYRVFYv359tRUphKilUmNhx+c3vIlWiaGE75O+5+VNL8vUwxpQ6RD/8MMPefjhh/Hy8gIgPj6ejh07AtCzZ09iY+VTV4g6L7QfvLgfgnob22f2g0FfYRNrjTWf9vkUG60Nz61/Tu56WM0qFeIrV67Ezc2NHj16mB5TFMV0f2F7e3vy8vKqp0IhRO12+TL9C6dgQSRs/OCqTXwdfJnddzbni8/L1MNqplIqsTzHI488gkqlQqVSkZiYSGBgIAkJCSQkJACwbt06YmNjefvttyvsFxcXh52dXaWLLSoqMh2+sWR1pR8gfamtzNIXRcEpdRUFPh3R27iDoQzUFedO7D6/mxlJM3gl5BUiXCNu+pLyOzG6ePEiERHX+XkpVTRy5EglKSlJefrpp5UdO3YoiqIokyZNUn799dertt2zZ0+VvldCQkKV9q8t6ko/FEX6UluZvS8Gg6IsHakov79x1VOnck/d8suYvR/VqCp9uVF2Vts88QkTJjBr1iyioqIoLS0lMjKyul5aCGFpDHpwCbjmZfr+jsbDL7FnYvkh6Yc7XFjdU+V54jExMaavFy1aVNWXE0LUBRotRF5xbPzkTsiMh4jHjPcuB5YkLuGP03/gZedFV7+uZirU8skVm0KImrcvBrb+F0ovmh6a3GMyQS5BMvWwiiTEhRA1b/BMeHyV8YZaBj2c2Y+DzoHP+n6GrdZWph5WgYS4EKLmqdXGRScAds833nvlr4P42Pswq+8szhef5/tj35u3Rgsl904RQtxZbYaDWgM+rQBo4dKUZfcuI9Ap0Lx1WSgZiQsh7ixrR+jwhPEEZ146fNqRxlnJqFQqUnNT+Tr+a3NXaFFkJC6EMB/T4swBAHx37Du+OvQV9lb2PNTkITMXZxkkxIUQ5uMSAI/+YmqOLdJwzKM1/97xb/wc/HDF1YzFWQY5nCKEqB0KL6Dd8TnT8CHYJZiXN73MyYsnzV1VrScjcSFE7WDrAs9sxV7nwKclFxjxcxT7zm4lkkhO5Z5i5r6ZOFs746RzwtnaGWdrZzr5dMLXwZeisiLyS/Nx0jmh0+jM3ZM7SkJcCFF72HsA4KP15osCNedz/gAgtzSXw6kbyUEhhzLTup2f+A7Ad8B0dqfv5tn1zwJgq7U1Br1B4d2wx2gVPpKE7ARW7Z+Hk703zm7BOOuccSouoJVPB+yd/dEb9KgVBZXG8iLR8ioWQtR9KhWh98ziTNzvALRwb8HPXv3B3hND93EUlBaQ8+2juBmMo+5gl2AmFlmR4+JPblBPckpyyIn/DvvjmyB8JCdyTvDNqbWUXLrk/7Jv3XrRdPBsVhxdwYc7/o2TxgZnRz+cdc44n97Pu2GP4tH9ZQ6k7yV+9XicA3vjHNIfZ40tzts/p0Gr4WhC+kJpIeycA8F9wLe1sX10Nfi1BddGUFZ81X3Wq4uEuBCidvJpSU6QBr/L7YGTAeOJPEedI44jvjNt6ufgR9TTeyvu33Y8qIyn/e4Juod7dD4UWduRY+dmDPnEnwjw7wZAM7dmPOrSihxbZ3Ks7ckpvkCmjT1aJ+PNujanbWIe5yBlpfHfJTuzO2AX0pdP987kl8Nf4XT6F5xdg3FWW+Gc+CtvdX0fVcQoDqSsR13mUSM/JglxIUTdZOdWse0fgQ1gA8bFm7u+YnqqjVcb2ty/5LovNabdC4xs+Sg5xTnkFOeQW5JLTnEOtkH3AtDIowWtQwaTU5pLbkk+Z4ovUOYdhCrM+Pw3p9Yx0nN0dfcQkBAXQoibslJb4WbjhpuN2zWfvzf4Xu4Nvve6+7/a6Q3OptbMvWFkiqEQQtQwTzvPGnttCXEhhLBgEuJCCGHBJMSFEMKCSYgLIYQFkxAXQggLJiEuhBAWTEJcCCEsmEpRFOVOfbO4uLg79a2EEKJOiYiIuObjdzTEhRBCVC85nCKEEBZMQlwIISxYrQnxnTt30rRpU3799dcKjw8ePJjXX3/dTFXdnilTphAdHc3AgQPp3bs30dHRjB071txlVcro0aM5ePAgACUlJURERPDll1+ano+OjiYxMfGGr5GcnEx0dHSN1nk91/pddO7cmfHjx5ulnpqUlpZGu3btiI6ONv2bPXt2hW3Gjx9PSUmJmSq8ublz5/Loo48ycuRIoqOjOXTo0HW3XbZsGaWlpXewult3O/2oNkotsWPHDmXgwIHKmDFjTI8dPnxY6du3rzJhwgQzVnb7vvvuO2Xq1KnmLqNKvvjiC2X+/PmKoihKbGysMnbsWGXkyJGKoihKUVGRctdddykGg+GGr5GUlGTax1yu/F3s2LFDGTdunFnrqQmnTp1Shg4dau4yKu3YsWNKVFSU6f2UkJCgDB48+Lrb33XXXUpRUdGdKu+W3W4/qkutGYkDNGvWjDNnzpCXlwfATz/9xODBg01fP/jggwwfPpw33niD0tJSVq5cyYsvvsjTTz/NoEGDWLly5Y1e3ixef/11tmzZAsCWLVtMf1X8/vvvREVFMXz4cKZNmwYYZ+8MGzaMESNG8MQTT5Cfn2+2urt27cqePXsA2Lx5M0OHDiUvL4+8vDz27dtHx44dWbVq1VV9yMzMZNSoUURHR/Ppp5+arf7rSU1N5Z///CdDhgxh1qxZgPGviuTkZACWLFnCrFmzSEtLY/DgwURHRzNv3jy++eYbhg4dSlRUFP/+97/N2YVbsnPnToYOHcqIESP44Ycf6NOnD8XFxeYu65ocHR05c+YM3377LRkZGYSFhfHtt9+ya9cu03tpyJAhnDhxghUrVpCVlVUr/6K6Xj+u9/6KiorixRdfZMiQIbzzzjuV/r61KsQBBgwYwJo1a1AUhYMHD9K2bVsuXLjArFmz+Prrr1myZAmOjo4sW7YMgPz8fL744gs+//xz5s6da+bqb83l/ixcuJAlS5aQkZHBtm3bWLduHYMGDWLRokUMHz6c3Nxcs9XYvHlzjh8/jqIo7N69m44dO9KlSxdiY2PZtWsX3bp1u2Yf5syZw7333ktMTAz9+vUzW/3XU1xczGeffcY333zDokWLbrhtVlYW8+fP58knn2TlypVMmjSJZcuWERQURFlZ2R2q+NYkJSVVOJySkZFBcXExixcv5v777zd3eTfk7e3N559/zt69e4mKimLgwIFs3LiRY8eOMXXqVGJiYhgwYACrVq1i6NCheHp68vHHH5u77Ktcrx/Xk5KSwgcffMCKFSvYsmULWVlZlfq+tW5RiMGDB/Puu+/SsGFD2rdvD4DBYCAkJAQHBwcAOnTowNatW2ndujXNmjUDwNfXt1Yf8wNQLs3mPHnyJOfOneOpp54CoKCggJMnT/LMM88wZ84cRo8ejbe3N+Hh4WarVa1W06xZM7Zs2YKnpyc6nY6ePXuyadMmDh8+TO/eva/Zh5SUFIYNGwZAu3btWLLk+qulmENoaCg6nXFdRq326re/csWMW39/f9O2kydPZsGCBXz00Ue0adOmwna1QUhICDExMab2zp07ady4sRkrunWpqak4ODgwebJx+bU///yTJ598kgkTJvDBBx9gZ2dHRkYG7dq1M3OlN3a9fnh6lt9L/Mr3TUBAgCnTPD09K/2XUq0biTds2JCLFy8SExPDfffdB4BKpSI5OZmLFy8CsGvXLtMbVPW3hU9rG51OZ/qETUhIAIzh4Ovry4IFC4iJiWHkyJG0adOGn376iQceeICYmBhCQ0NZvny5OUunW7dufPHFF/To0QMwXmyQkJCAwWC4bh+Cg4PZt28fYHwT1zbXer9c63cExg+yy5YvX857773HokWLSExMNPWxNruy/trsyJEjvP/++6ZBWOPGjXFycuI///kP//nPf5gyZQpeXl6mAFSpVBgMBnOWfE3X64eLi8s131/VlV21biQOcPfdd/Pjjz/SuHFjTp06haurK/feey+jRo1CrVYTEBDAK6+8ctVMltpo6NChvPnmm/z8888EBgYC4ObmxqOPPkp0dDR6vZ4GDRowaNAgSkpKmDhxIra2tqjVat5//32z1t61a1cmTpzIRx99BBjDztHRkbCwsOv2YcyYMbz66qv89ttv+Pv7m7X+WzVq1Cjee+89/Pz88PLyuuY2TZs2ZcSIEdjb2+Pt7U3r1q3vcJV114ABA0hOTuahhx7Czs4ORVF47bXX2L17N4888gi2trZ4eHiQmZkJQPv27Xnqqaf43//+V6sGcdfrh5WV1U3fX1UhV2wKIYQFs4y/t4QQQlyThLgQQlgwCXEhhLBgEuJCCGHBJMSFEMKCSYgLIYQFkxAXQggLJiEuhBAW7P+rE7/QnUPUPgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_weekday = data.groupby(data.index.dayofweek).mean()\n",
    "by_weekday.index = ['Mon', 'Tues', 'Wed', 'Thurs', 'Fri', 'Sat', 'Sun']\n",
    "by_weekday.plot(style=['-', ':', '--']);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows a strong distinction between weekday and weekend totals, with around twice as many average riders crossing the bridge on Monday through Friday than on Saturday and Sunday.\n",
    "\n",
    "With this in mind, let's do a compound `groupby` and look at the hourly trends on weekdays versus weekends.\n",
    "We'll start by grouping by flags marking the weekend and the time of day:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "weekend = np.where(data.index.weekday < 5, 'Weekday', 'Weekend')\n",
    "by_time = data.groupby([weekend, data.index.time]).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we'll use some of the Matplotlib tools that will be described in [Multiple Subplots](04.08-Multiple-Subplots.ipynb) to plot two panels side by side, as shown in the following figure:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1008x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "fig, ax = plt.subplots(1, 2, figsize=(14, 5))\n",
    "by_time.loc['Weekday'].plot(ax=ax[0], title='Weekdays',\n",
    "                            xticks=hourly_ticks, style=['-', ':', '--'])\n",
    "by_time.loc['Weekend'].plot(ax=ax[1], title='Weekends',\n",
    "                            xticks=hourly_ticks, style=['-', ':', '--']);"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The result shows a bimodal commuting pattern during the work week, and a unimodal recreational pattern during the weekends.\n",
    "It might be interesting to dig through this data in more detail and examine the effects of weather, temperature, time of year, and other factors on people's commuting patterns; for further discussion, see my blog post [\"Is Seattle Really Seeing an Uptick in Cycling?\"](https://jakevdp.github.io/blog/2014/06/10/is-seattle-really-seeing-an-uptick-in-cycling/), which uses a subset of this data.\n",
    "We will also revisit this dataset in the context of modeling in [In Depth: Linear Regression](05.06-Linear-Regression.ipynb)."
   ]
  }
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